Decreasing Competence?

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 17 Nov 2010 09:04:39 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6.htm/, Retrieved Wed, 17 Nov 2010 10:09:59 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

9 41 38 13 12 14 12 53 32 9 39 32 16 11 18 11 83 51 9 30 35 19 15 11 14 66 42 9 31 33 15 6 12 12 67 41 9 34 37 14 13 16 21 76 46 9 35 29 13 10 18 12 78 47 9 39 31 19 12 14 22 53 37 9 34 36 15 14 14 11 80 49 9 36 35 14 12 15 10 74 45 9 37 38 15 9 15 13 76 47 9 38 31 16 10 17 10 79 49 9 36 34 16 12 19 8 54 33 9 38 35 16 12 10 15 67 42 9 39 38 16 11 16 14 54 33 9 33 37 17 15 18 10 87 53 9 32 33 15 12 14 14 58 36 9 36 32 15 10 14 14 75 45 9 38 38 20 12 17 11 88 54 9 39 38 18 11 14 10 64 41 9 32 32 16 12 16 13 57 36 9 32 33 16 11 18 9.5 66 41 9 31 31 16 12 11 14 68 44 9 39 38 19 13 14 12 54 33 9 37 39 16 11 12 14 56 37 9 39 32 17 12 17 11 86 52 9 41 32 17 13 9 9 80 47 9 36 35 16 10 16 11 76 43 9 33 37 15 14 14 15 69 44 9 33 33 16 12 15 14 78 45 9 34 33 14 10 11 13 67 44 9 31 31 15 12 16 9 80 49 9 27 32 12 8 13 15 54 33 9 37 31 14 10 17 10 71 43 9 34 37 16 12 15 11 84 54 9 34 30 14 12 14 13 74 42 9 32 33 10 7 16 8 71 44 9 29 31 10 9 9 20 63 37 9 36 33 14 12 15 12 71 43 9 29 31 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 16 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 8.35046999717002 -0.40459931201988month[t] + 0.0347834365471902Connected[t] + 0.0433406498517765Separate[t] + 0.576062196352003Software[t] + 0.0796225833189896Happiness[t] -0.0261020091912720Depression[t] + 0.0282902834264182Belonging[t] -0.0326511809003049Belonging_Final[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 8.35046999717002 2.61698 3.1909 0.001596 0.000798 month -0.40459931201988 0.159517 -2.5364 0.011797 0.005898 Connected 0.0347834365471902 0.034756 1.0008 0.317879 0.15894 Separate 0.0433406498517765 0.03528 1.2285 0.220402 0.110201 Software 0.576062196352003 0.052771 10.9163 0 0 Happiness 0.0796225833189896 0.057845 1.3765 0.169879 0.08494 Depression -0.0261020091912720 0.042437 -0.6151 0.53905 0.269525 Belonging 0.0282902834264182 0.037766 0.7491 0.454497 0.227248 Belonging_Final -0.0326511809003049 0.056098 -0.582 0.561053 0.280527 Multiple Linear Regression - Regression Statistics Multiple R 0.664098147499979 R-squared 0.441026349512904 Adjusted R-squared 0.423489921262328 F-TEST (value) 25.1491548456242 F-TEST (DF numerator) 8 F-TEST (DF denominator) 255 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.86477720145105 Sum Squared Residuals 886.735472818163 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 15.9509274269776 -2.95092742697758 2 16 15.6181828665754 0.381817133424643 3 19 16.9166643716608 2.08333562833916 4 15 11.8729748073647 3.12702519263529 5 14 16.3580519877682 -2.35805198776815 6 13 14.7360162717571 -1.73601627175708 7 19 15.1537000085073 3.84629999149269 8 15 17.0077560505479 -2.00775605054788 9 14 15.9484454966395 -1.94844549663945 10 15 14.2980364711644 0.701963528835628 11 16 14.8626172377916 1.13738276220843 12 16 16.1018077007215 -0.101807700721472 13 16 15.3893109658985 0.61068903410149 14 16 15.4079786083135 0.592021391686455 15 17 18.0043950630554 -1.00439506305541 16 15 15.3798159239431 -0.379815923943112 17 15 14.5105588177225 0.489441182277545 18 20 16.3833808166030 3.61661918339702 19 18 15.3748348655024 2.62516513449760 20 16 15.4935321664942 0.506467833505832 21 16 15.3027694651376 0.697230534862387 22 16 15.0411768247971 0.958823175202857 23 19 16.4530618527621 2.54693814723788 24 16 14.9892378950466 1.01076210495340 25 17 16.1668421489873 0.833157851012718 26 17 16.2212087742073 0.778791225792692 27 16 14.9717246067166 1.02827539328339 28 15 16.7639680138983 -1.7639680138983 29 16 15.7661669842349 0.233833015765089 30 14 14.0778957672031 -0.0778957672031147 31 15 15.7460272839639 -0.746027283963944 32 12 12.7373771224324 -0.737377122432354 33 14 14.7974186192349 -0.797418619234864 34 16 15.9285001202188 0.0714998797812121 35 14 15.6022003060943 -1.60220030609430 36 10 12.9219264013097 -2.92192640130971 37 10 13.0146729900312 -3.01467299003119 38 14 15.7899916900747 -1.78999169007471 39 16 14.5626490012885 1.43735099871148 40 16 14.4721133266155 1.52788667338445 41 16 14.7271846001186 1.27281539988142 42 14 15.8283533347221 -1.82835333472214 43 20 17.5740121132113 2.42598788678866 44 14 14.1663108781857 -0.166310878185714 45 14 14.4929035444449 -0.492903544444942 46 11 15.5799467762799 -4.57994677627988 47 14 16.6042289856471 -2.60422898564706 48 15 15.1942294341289 -0.194229434128941 49 16 15.6290939132253 0.37090608677469 50 14 15.7309335277757 -1.73093352777567 51 16 17.1471512900765 -1.14715129007648 52 14 14.2216774089203 -0.221677408920321 53 12 15.1453403734528 -3.14534037345284 54 16 15.8695524007130 0.130447599286978 55 9 11.3645521776952 -2.36455217769524 56 14 12.4006749187661 1.59932508123387 57 16 16.0107717121744 -0.0107717121744176 58 16 15.5876443588646 0.412355641135398 59 15 15.2486517151036 -0.248651715103637 60 16 14.3009604497181 1.69903955028191 61 12 11.5680405712374 0.431959428762605 62 16 15.8480933851408 0.151906614859215 63 16 16.7320968224482 -0.732096822448167 64 14 14.8491657946905 -0.849165794690488 65 16 15.4532184988394 0.546781501160557 66 17 15.786728295299 1.2132717047010 67 18 16.0663974549983 1.93360254500172 68 18 14.2236008420055 3.7763991579945 69 12 15.7039588645577 -3.70395886455774 70 16 15.4478381365797 0.552161863420335 71 10 13.0291955081803 -3.02919550818028 72 14 14.6990097947249 -0.699009794724867 73 18 16.8215387173465 1.17846128265351 74 18 17.0473858368980 0.952614163101954 75 16 15.0618538666444 0.93814613335562 76 17 13.2686578002251 3.73134219977488 77 16 16.2744534843814 -0.274453484381412 78 16 14.2740950485574 1.72590495144259 79 13 15.0334785537398 -2.03347855373977 80 16 14.9247697671811 1.07523023281887 81 16 15.5655347518703 0.434465248129714 82 16 15.5369752066879 0.463024793312053 83 15 15.5563789647738 -0.556378964773841 84 15 14.7091761878920 0.290823812108049 85 16 13.9756487604527 2.02435123954728 86 14 14.0085733239176 -0.00857332391764704 87 16 15.2208307489935 0.77916925100649 88 16 14.7065934689149 1.29340653108514 89 15 14.3547510265222 0.645248973477802 90 12 13.7050807374168 -1.70508073741682 91 17 16.6782641138012 0.321735886198799 92 16 15.7430202889882 0.256979711011831 93 15 15.0195639849035 -0.0195639849034504 94 13 14.9635980286721 -1.96359802867209 95 16 14.6973726642945 1.30262733570547 96 16 15.6830629570827 0.316937042917323 97 16 13.5822989199044 2.41770108009563 98 16 15.7685105700457 0.231489429954300 99 14 14.3198580815311 -0.319858081531108 100 16 17.0247038337605 -1.02470383376054 101 16 14.5787973362837 1.42120266371627 102 20 17.4687674897040 2.53123251029596 103 15 14.0523046259387 0.947695374061293 104 16 14.9563332782169 1.04366672178305 105 13 14.8011674216603 -1.80116742166032 106 17 15.6582118368939 1.34178816310611 107 16 15.7149454231363 0.285054576863653 108 16 14.2651695279916 1.73483047200845 109 12 12.1445025399444 -0.144502539944429 110 16 15.2700317301080 0.729968269891974 111 16 16.0190228965521 -0.0190228965520536 112 17 14.9728991243270 2.02710087567298 113 13 14.6235262603592 -1.62352626035921 114 12 14.5611065152376 -2.56110651523763 115 18 16.182566405197 1.81743359480300 116 14 15.8742392027801 -1.8742392027801 117 14 13.0634243613986 0.93657563860137 118 13 14.7664152300671 -1.7664152300671 119 16 15.57275380411 0.427246195889985 120 13 14.4067456846915 -1.40674568469151 121 16 15.4391647652899 0.56083523471014 122 13 15.8911672712233 -2.89116727122331 123 16 16.9042581868609 -0.904258186860862 124 15 15.8921534889468 -0.892153488946773 125 16 16.9022361707383 -0.9022361707383 126 15 14.7036320628420 0.296367937158032 127 17 15.6113104275905 1.38868957240952 128 15 13.9838607966409 1.01613920335906 129 12 14.7440838384905 -2.74408383849047 130 16 13.9936422626157 2.00635773738434 131 10 13.7576725915982 -3.75767259159823 132 16 13.5176321144828 2.48236788551718 133 12 14.2161631177914 -2.21616311779137 134 14 15.6237963714392 -1.62379637143924 135 15 15.1698667563622 -0.169866756362208 136 13 12.1421557046257 0.857844295374258 137 15 14.5009469200994 0.499053079900579 138 11 13.4686249246835 -2.46862492468351 139 12 13.0157243738587 -1.01572437385866 140 11 13.4194465342762 -2.41944653427621 141 16 12.8831315054463 3.11686849455368 142 15 13.7558505714002 1.24414942859978 143 17 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215 16 14.5684829589542 1.43151704104577 216 14 13.0731346341900 0.92686536580997 217 17 14.6746123754413 2.32538762455872 218 14 13.9423598225196 0.0576401774804326 219 13 11.8905028372228 1.10949716277721 220 15 15.2059654363509 -0.205965436350911 221 13 14.6710171706430 -1.67101717064297 222 14 14.1319205655599 -0.131920565559859 223 15 14.3001182465164 0.699881753483601 224 12 13.1092937744911 -1.10929377449114 225 13 12.7116664065425 0.288333593457546 226 8 11.7472131342750 -3.74721313427504 227 14 13.8015801739644 0.198419826035621 228 14 13.0202674279236 0.97973257207643 229 11 12.2297591833158 -1.22975918331585 230 12 12.8968382776945 -0.896838277694458 231 13 11.2452084819542 1.75479151804577 232 10 13.2130657616632 -3.21306576166324 233 16 11.3677174009185 4.63228259908155 234 18 15.9474161696061 2.05258383039394 235 13 13.8693288638037 -0.869328863803698 236 11 13.3430027385425 -2.34300273854246 237 4 10.9726967298314 -6.97269672983141 238 13 14.2678178481433 -1.26781784814330 239 16 14.3069070865238 1.6930929134762 240 10 11.6823923405169 -1.68239234051694 241 12 12.2879119795221 -0.287911979522077 242 12 13.5011921116947 -1.50119211169469 243 10 8.85142253316746 1.14857746683254 244 13 11.1028627902225 1.89713720977750 245 15 13.7092481004796 1.29075189952042 246 12 11.9685057317415 0.0314942682585147 247 14 12.8777319860027 1.12226801399732 248 10 12.5358000227952 -2.53580002279515 249 12 10.7624130858841 1.23758691411588 250 12 11.7393909440044 0.260609055995635 251 11 11.9220624304821 -0.922062430482096 252 10 11.7304631725709 -1.73046317257095 253 12 11.4019661994277 0.59803380057231 254 16 12.8825562632821 3.11744373671786 255 12 13.3765522827852 -1.37655228278524 256 14 13.9048290853521 0.0951709146478995 257 16 14.5198797799651 1.48012022003486 258 14 11.6682798848408 2.33172011515922 259 13 14.3566901566030 -1.35669015660303 260 4 9.3833458059488 -5.38334580594881 261 15 13.8472593284005 1.15274067159949 262 11 15.1083609537208 -4.1083609537208 263 11 11.3476291943178 -0.347629194317837 264 14 12.9144494714043 1.08555052859575 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.249272273417129 0.498544546834257 0.750727726582871 13 0.168954109350983 0.337908218701965 0.831045890649017 14 0.188652474482696 0.377304948965393 0.811347525517304 15 0.113822199570940 0.227644399141881 0.88617780042906 16 0.0773581785451667 0.154716357090333 0.922641821454833 17 0.111089921688014 0.222179843376027 0.888910078311986 18 0.350297244008135 0.70059448801627 0.649702755991865 19 0.273076148217577 0.546152296435153 0.726923851782423 20 0.20036477979948 0.40072955959896 0.79963522020052 21 0.147329038244943 0.294658076489885 0.852670961755057 22 0.142429822152623 0.284859644305245 0.857570177847377 23 0.329613610691874 0.659227221383749 0.670386389308126 24 0.396108111170374 0.792216222340749 0.603891888829626 25 0.364643782622245 0.729287565244489 0.635356217377755 26 0.343013785262702 0.686027570525403 0.656986214737298 27 0.419522216901141 0.839044433802282 0.580477783098859 28 0.431039269566414 0.862078539132827 0.568960730433586 29 0.411178257665486 0.822356515330972 0.588821742334514 30 0.444562864383298 0.889125728766596 0.555437135616702 31 0.389182053396219 0.778364106792438 0.610817946603781 32 0.346620753254168 0.693241506508337 0.653379246745831 33 0.316677130929610 0.633354261859221 0.68332286907039 34 0.275674898300487 0.551349796600974 0.724325101699513 35 0.233139524871994 0.466279049743987 0.766860475128006 36 0.363623899560609 0.727247799121217 0.636376100439392 37 0.400480018023808 0.800960036047616 0.599519981976192 38 0.392518456881072 0.785036913762145 0.607481543118928 39 0.42064689819391 0.84129379638782 0.57935310180609 40 0.394036668820201 0.788073337640401 0.6059633311798 41 0.352608510695984 0.705217021391968 0.647391489304016 42 0.317815560782687 0.635631121565375 0.682184439217313 43 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0.00537338633076858 0.0107467726615372 0.994626613669231 229 0.00380717833516555 0.0076143566703311 0.996192821664834 230 0.00266471220180094 0.00532942440360187 0.9973352877982 231 0.00267158170978464 0.00534316341956929 0.997328418290215 232 0.00494336392668188 0.00988672785336376 0.995056636073318 233 0.0259467354434979 0.0518934708869958 0.974053264556502 234 0.0384579406778061 0.0769158813556122 0.961542059322194 235 0.0277187884740289 0.0554375769480579 0.97228121152597 236 0.0239618139472759 0.0479236278945519 0.976038186052724 237 0.187771175358450 0.375542350716899 0.812228824641550 238 0.147044861176903 0.294089722353805 0.852955138823097 239 0.130216166245390 0.260432332490781 0.86978383375461 240 0.0988684339233115 0.197736867846623 0.901131566076689 241 0.0766001416875223 0.153200283375045 0.923399858312478 242 0.0614634178432018 0.122926835686404 0.938536582156798 243 0.0562097761470191 0.112419552294038 0.94379022385298 244 0.103562675764338 0.207125351528676 0.896437324235662 245 0.0907837726734566 0.181567545346913 0.909216227326543 246 0.0696382930217311 0.139276586043462 0.930361706978269 247 0.0465296435673761 0.0930592871347521 0.953470356432624 248 0.0378059998785717 0.0756119997571434 0.962194000121428 249 0.0340747449655495 0.068149489931099 0.96592525503445 250 0.0414488690680374 0.0828977381360748 0.958551130931963 251 0.0218224478596724 0.0436448957193448 0.978177552140328 252 0.0547426763653312 0.109485352730662 0.945257323634669 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.0414937759336100 NOK 5% type I error level 85 0.352697095435685 NOK 10% type I error level 133 0.551867219917012 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/104hd41289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/104hd41289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/1xggs1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/1xggs1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/2qqfd1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/2qqfd1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/3qqfd1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/3qqfd1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/4qqfd1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/4qqfd1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/5qqfd1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/5qqfd1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/6izfg1289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/6izfg1289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/7tqe11289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/7tqe11289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/8tqe11289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/8tqe11289984662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/94hd41289984662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t12899849862wg0yilynfjlce6/94hd41289984662.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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